Coverage and data plane separated super-dense heterogeneous cellular network user access method

Abstract

The invention belongs to the technical field of user accessing to a super-dense heterogeneous cellular network, and discloses a coverage and data plane separated super-dense heterogeneous cellular network user access method. The method comprises the following steps: building a microcellular base station system model based on a Poisson cluster process; assisting, by multiple micro base stations distributed around the same hotspot, a user in access service; building a macro base station system model based on a Poisson point process; on the premise that a macro base station is mainly used for completing a coverage function and the micro base stations are used for high-rate data transmission, respectively deducting an SINR (Signal to Interference plus Noise Ratio) distribution model and an interference distribution model which are received by a macro base station user and a micro base station user; deducting an average accessible rate when users in a network are served by different types of base stations by using the SINR distribution model and a known distance distribution model; and deducting a reliable closed upper bound and lower bound of Laplace transformation of the interferencedistribution model. A data simulation result shows that the coverage and data plane separated super-dense heterogeneous cellular network user access method provided by the invention can achieve a higher data rate.

Description

technical field [0001] The invention belongs to the technical field of ultra-dense heterogeneous cellular network user access, and in particular relates to an ultra-dense heterogeneous cellular network user access method with separation of coverage and data plane. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Stochastic geometry is a mathematical tool with wide applicability to describe the spatial distribution of networks. It is suitable for modeling the spatial distribution of ultra-dense base stations and users. It can accurately describe the theoretical relationship between network performance indicators and network deployment parameters. , which can provide a reference for the deployment of ultra-dense heterogeneous cellular networks in the future. [0004] In traditional mobile wireless networks, users are served by a single user that meets their service indicators, but with the ultra-dense d...

AI Technical Summary

Problems solved by technology

[0007] (1) In the traditional mobile wireless network, users are served by a single user that meets their service indicators, but with the ultra-dense deployment of base stations, the distance between base stations is shortened, forming a worse interference environment. then apply

[0008] (2) Ultra-densely deployed micro base stations have the characteristic of being clustered in hotspot areas, which not only does not bring benefits to the existing traditional network access methods, but also seriously interferes with the communication between the serving base station and users

[0009] (3) The future network is a structure in w...

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